[BOOK][B] Filtering complex turbulent systems
AJ Majda, J Harlim - 2012 - books.google.com
Many natural phenomena ranging from climate through to biology are described by complex
dynamical systems. Getting information about these phenomena involves filtering noisy data …
dynamical systems. Getting information about these phenomena involves filtering noisy data …
Launching drifter observations in the presence of uncertainty
Determining the optimal locations for placing extra observational measurements has
practical significance. However, the exact underlying flow field is never known in practice …
practical significance. However, the exact underlying flow field is never known in practice …
Information quantity evaluation of nonlinear time series processes and applications
JE Contreras-Reyes - Physica D: Nonlinear Phenomena, 2023 - Elsevier
Among several models proposed in the time series literature, the Self-Exciting Threshold
Autoregressive (SETAR) model is non-linear and considers threshold values to model time …
Autoregressive (SETAR) model is non-linear and considers threshold values to model time …
Uncertainty quantification of nonlinear Lagrangian data assimilation using linear stochastic forecast models
Lagrangian data assimilation exploits the trajectories of moving tracers as observations to
recover the underlying flow field. One major challenge in Lagrangian data assimilation is the …
recover the underlying flow field. One major challenge in Lagrangian data assimilation is the …
LEMDA: A Lagrangian‐Eulerian multiscale data assimilation framework
Lagrangian trajectories are widely used as observations for recovering the underlying flow
field via Lagrangian data assimilation (DA). However, the strong nonlinearity in the …
field via Lagrangian data assimilation (DA). However, the strong nonlinearity in the …
Stochastic modeling of decadal variability in ocean gyres
Decadal large‐scale low‐frequency variability of the ocean circulation due to its nonlinear
dynamics remains a big challenge for theoretical understanding and practical ocean …
dynamics remains a big challenge for theoretical understanding and practical ocean …
Lagrangian descriptors with uncertainty
Lagrangian descriptors provide a global dynamical picture of the geometric structures for
arbitrarily time-dependent flows with broad applications. This paper develops a …
arbitrarily time-dependent flows with broad applications. This paper develops a …
Combining stochastic parameterized reduced‐order models with machine learning for data assimilation and uncertainty quantification with partial observations
A hybrid data assimilation algorithm is developed for complex dynamical systems with
partial observations. The method starts with applying a spectral decomposition to the entire …
partial observations. The method starts with applying a spectral decomposition to the entire …
Using machine learning to discern eruption in noisy environments: A case study using CO2‐driven cold‐water geyser in Chimayó, New Mexico
We present an approach based on machine learning (ML) to distinguish eruption and
precursory signals of Chimayó geyser (New Mexico, USA) under noisy environmental …
precursory signals of Chimayó geyser (New Mexico, USA) under noisy environmental …
Forecasting turbulent modes with nonparametric diffusion models: Learning from noisy data
T Berry, J Harlim - Physica D: Nonlinear Phenomena, 2016 - Elsevier
In this paper, we apply a recently developed nonparametric modeling approach, the
“diffusion forecast”, to predict the time-evolution of Fourier modes of turbulent dynamical …
“diffusion forecast”, to predict the time-evolution of Fourier modes of turbulent dynamical …